Artificial Intelligence for Decentralized Orchestration in the Physical Internet: Opportunities, Business Trade-offs, and Risks in Road Freight Logistics

The Physical Internet (PI) has been conceptualized as a disruptive response to inefficiencies and fragmentation in logistics. Through modularization, interoperability, and open hubs, PI envisions a hyperconnected and sustainable road freight ecosystem. Yet its realization critically depends on Artificial Intelligence (AI) as the principal enabler of decentralized orchestration. This paper develops a theoretical framework for AI-driven PI logistics. Our novelty lies in a unified orchestration lens that jointly tackles fairness, data-sharing governance, and cybersecurity in multi-agent PI systems at scale. We argue that orchestration will require multi-agent AI systems capable of decentralized load consolidation, routing optimization, and hub allocation. These agents interact with IoT-equipped vehicles and containers, generating high-frequency data streams for predictive modeling and adaptive decision making. Complementarily, blockchain infrastructures secure transactions, while smart contracts provide automated enforcement of collaborative agreements. We highlight implications for business performance: trade-offs between cost-to-serve, service-level adherence, and collaboration ROI must be considered alongside sustainability gains. Evidence from existing multi-agent last-mile simulations and large-scale PI pilot projects in France shows quantifiable improvements (e.g., reduced vehicle kilometers traveled, higher load factors), offering empirical support. Scenario KPIs such as ΔVKT, Δload factor, and fairness indices are proposed to assess impact. Nevertheless, embedding AI in PI road freight introduces challenges: (i) ensuring algorithmic fairness to protect SMEs, (ii) implementing robust data governance and interoperability frameworks, and (iii) guaranteeing cybersecurity and resilience against adversarial threats. To address these, we propose research propositions on scalable orchestration, governance for trustworthy data and algorithms, and risk mitigation strategies. A conceptual framework and architecture diagram will be provided to integrate technical, organizational, and governance perspectives. By outlining opportunities, business trade-offs, and systemic risks, this paper establishes a conceptual agenda for research and policy, aligned with the EU AI Act, digitalization, and green transition priorities.